Artificial keys for botanical identification using a multilayer perceptron neural network (MLP)

Clark, J. Y. and Warwick, K. (1998) Artificial keys for botanical identification using a multilayer perceptron neural network (MLP). Artificial Intelligence Review, 12 (1-3). pp. 95-115. ISSN 0269-2821 doi: 10.1023/A:1006544506273

Abstract/Summary

In this paper, practical generation of identification keys for biological taxa using a multilayer perceptron neural network is described. Unlike conventional expert systems, this method does not require an expert for key generation, but is merely based on recordings of observed character states. Like a human taxonomist, its judgement is based on experience, and it is therefore capable of generalized identification of taxa. An initial study involving identification of three species of Iris with greater than 90% confidence is presented here. In addition, the horticulturally significant genus Lithops (Aizoaceae/Mesembryanthemaceae), popular with enthusiasts of succulent plants, is used as a more practical example, because of the difficulty of generation of a conventional key to species, and the existence of a relatively recent monograph. It is demonstrated that such an Artificial Neural Network Key (ANNKEY) can identify more than half (52.9%) of the species in this genus, after training with representative data, even though data for one character is completely missing.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/17816
Item Type	Article
Refereed	Yes
Divisions	Science
Uncontrolled Keywords	aizoaceae, expert system, identification, Iris, key, lithops, mesembryanthemaceae, multilayer perceptrons, neural network
Publisher	Springer Verlag
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